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I would like to train object detection model (e.g. YOLO) for images that contain anomalies. The anomalies are essentially the holes in a surface of different sizes. How do I label correctly such anomalies? Do I put the bounding boxes over each small hole or should I group smaller anomalies into one?

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When labeling anomalies in images, it's important to be consistent and clear in your approach. In the case of holes in a surface, you have a few options for labeling. One approach, like you mentioned, is to label each individual hole with its own bounding box which approach allows for more precise detection of each anomaly and can be useful if you need to know the location and size of each hole.

Alternatively, you could group smaller anomalies together into one bounding box. This approach may be more efficient and easier to label, but may result in less precise detection of individual anomalies. Ultimately, the approach you choose will depend on your specific use case and the level of precision required for detection.

Do you have any more information about the holes and your end goal? Being more specific or providing examples may help others answer your question.

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